GFW analyzes and provides online interactive maps of the behavior of fishing vessels from global AIS and VMS data. AIS was originally designed as a tool to avoid collisions at sea as part of the IMO Safety of Life At Sea Treaty [SOLAS Treaty, Chapter V; (37)]. Vessels equipped with an AIS transponder signal their position and vessel identification data such as IMO number, maritime mobile security information number, call sign, ship type, speed and course over ground, and other information to ships nearby carrying the transponders as well as to receiving ground stations and low-orbit satellites. Signal transmission frequencies vary with speeds between a few seconds and a few minutes. These high-resolution tracking data are then analyzed by GFW to assess ship movements and behavior, using neural network algorithms and logistic models to classify different fishing gear types as well as the points in space and time where individual vessels deploy their fishing gear (16). Data used for this study were derived from the logistic regression model 1.1 ( It is worth noting, however, that GFW only uses satellite-based AIS data, which have limitations such as a maximum number of individual signals that can be detected simultaneously, heterogenous satellite spatiotemporal coverage, or gaps near coastlines, where shore-based stations receive the signal that the satellite can no longer detect. It is unlikely for areas in the high seas to experience satellite channel saturation, and vessel AIS signals are also unlikely to be detected by shore-based stations. Fishing effort is detected and calculated, as hours of fishing, for individual fishing gear types: (i) pelagic longlines, (ii) trawls, (iii) purse seines, (iv) fixed gear, and (v) other types of fishing gear. However, each of these is subject to behavior classification errors. For this study, estimates of global pelagic fishing effort for the years 2015 and 2016 were extracted from the GFW database, including vessels from 114 countries and territories.

We filtered the GFW fishing effort estimates spatially to only include longlining events in the high seas. Within the high seas, fishing effort by pelagic longliners accounted for 88.9 and 80.4% of the quantified fishing effort (hours) in ABNJ across all gear groups in 2015 and 2016, respectively (fig. S1). The dominance of longline fishing effort in ABNJ and its known negative impacts on multiple nontarget species (38) underscore the importance of understanding the potential drivers of its global distribution (Fig. 1). Hence, we focused on longlines only in our modeling efforts, particularly the distribution of fishing events rather than fishing intensity.

According to GFW fishing effort estimates, 45 to 50 fishing States and territories deployed longlines in ABNJ throughout 2015 and 2016. We refined the list of countries to only include those that accounted for the >80% of the observed fishing effort; this reduced the list of fishing States and territories to five (fig. S2). We further selected the fishing effort data used to build the environmental niche models by only including these five major fishing States and territories. The fishing effort applied by these countries was aggregated spatially to 1° by 1° cells for 2015 and 2016 (Fig. 1) given the global extent of the analysis (39) and then partitioned temporally into 24 months (Fig. 2). Environmental data layers specific to each month were then used to run each of the 24 monthly environmental niche models. The use of monthly averages and monthly climatologies for certain environmental variables inevitably resulted in the loss of some fine-scale environmental features (for example, mesoscale oceanic eddies and frontal zones) that may influence the distribution of fishing effort at submonthly time steps. Future analysis at finer spatiotemporal resolution may allow the inclusion of more information on dynamic oceanographic features. For the purpose of this study, we focused on the monthly environmental variability on the distribution of fishing effort.

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